chunk_id stringlengths 3 7 | chunk stringlengths 1 823 | source_url stringclasses 416
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32_27 | We use the same methodology to classify tweets in our dataset as blackmarket or genuine.
Spam Detection 2: For baseline 2, we consider the approach proposed by Rajdev et. al. BIBREF11 . They proposed flat and hierarchical classifications approaches with few of the standard set of features which can classify spam, fak... | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 27 | 14,386 | 14,932 |
32_28 | This feature vector is then fed to state-of-the-art machine learning classifiers - Random Forest (RF), Multi-layer Perceptron (MLP), and Support Vector Machine (SVM).
Evaluation Setup
We consider the problem as a binary classification problem, where the tweets are classified into two classes - blackmarket and genuin... | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 28 | 14,932 | 15,559 |
32_29 | The average results are reported after 5-fold cross-validation.
Experimental Results
As shown in Table TABREF29 , we observe that the multitask learning based model which uses the Tweet2Vec encoding and the content features as inputs to two separate tasks outperforms all the baselines, achieving an F1-score of 0.89 ... | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 29 | 15,559 | 16,135 |
32_30 | The percentage of each class in the false negatives is as follows: Promotional - 23.29%, Politics - 10.96%, Entertainment - 21.92%, News - 9.59%, Spam - 5.48%, and Others - 28.77%. We observe that the tweets belonging to the category Others are difficult to classify since they are similar to genuine tweets in terms of... | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 30 | 16,135 | 16,617 |
32_31 |
Conclusion
In this paper, we presented a novel multitask learning approach to solve the problem of identification of tweets that are submitted to blackmarket services, without the use of any temporal features. | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 31 | 16,617 | 16,829 |
32_32 | To sum up, our contributions are three-fold: (i) Characterization: We proposed 12 tweet content based features that are useful in the task of identifying blackmarket tweets, (ii) Classification: We developed a novel Multitask Learning based model to classify tweets as blackmarket tweets or genuine tweets, (iii) Datase... | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 32 | 16,829 | 17,283 |
32_33 |
Acknowledgements
The work was partially funded by DST (ECR/2017/00l691, DST/INT/UK/P158/2017), Ramanujan Fellowship, and the Infosys Centre of AI, IIIT-Delhi, India.
Fig. 1. Architecture of our proposed multitask learning model for the detection of blackmarket tweets.
TABLE II PERFORMANCE OF THE COMPETING METHODS. | https://arxiv.org/abs/1907.04072 | Multitask Learning for Blackmarket Tweet Detection | 33 | 17,283 | 17,603 |
33_0 | Cross-Lingual Natural Language Generation via Pre-Training
In this work we focus on transferring supervision signals of natural language generation (NLG) tasks between multiple languages. We propose to pretrain the encoder and the decoder of a sequence-to-sequence model under both monolingual and cross-lingual setting... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 0 | 0 | 599 |
33_1 | Then the sequence-to-sequence model trained in a single language can be directly evaluated beyond that language (i.e., accepting multi-lingual input and producing multi-lingual output). Experimental results on question generation and abstractive summarization show that our model outperforms the machine-translation-bas... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 1 | 599 | 1,186 |
33_2 |
Introduction
Learning natural language generation (NLG) models heavily relies on annotated training data. However, most available datasets are collected in a single language (typically English), which restricts deploying the applications to other languages. In this work, we aim at transferring the supervision of a m... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 2 | 1,186 | 1,613 |
33_3 |
Various methods have been proposed over the years to learn universal cross-lingual word embeddings BIBREF0, BIBREF1, BIBREF2 or sentence encoders BIBREF3, BIBREF4, BIBREF5, which tries to encode multilingual texts into a single shared vector space. Despite achieving promising results on cross-lingual classification p... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 3 | 1,613 | 2,227 |
33_4 | So both encoder and decoder should be pre-trained together. Second, the many-to-many nature of cross-lingual NLG increases language pairs with the square of the number of languages. Third, the prediction space of cross-lingual NLG is much larger than classification tasks, which makes the knowledge transfer of decoders... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 4 | 2,227 | 2,741 |
33_5 | For example, the input written in other languages is first translated to English, and fed into the NLG model that is trained by English data. Then the generated English text is translated back to the target language. Another strand of work employs MT to generate pseudo training data for other language pairs that are l... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 5 | 2,741 | 3,413 |
33_6 |
In this paper, we propose a cross-lingual pre-trained model (named as Xnlg) in order to transfer monolingual NLG supervision to other pre-trained languages by fine-tuning. Specifically, Xnlg shares the same sequence-to-sequence model across languages, and is pre-trained with both monolingual and cross-lingual objecti... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 6 | 3,413 | 3,987 |
33_7 | The proposed model enables us to fine-tune the pre-trained model on monolingual NLG training data, and then evaluate it beyond a single language, including zero-shot cross-lingual generation. Besides, we explore several fine-tuning strategies to make a compromise between cross-lingual ability and task ability. In addi... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 7 | 3,987 | 4,500 |
33_8 | Experimental results on the NLG tasks show that Xnlg achieves competitive performance compared with the machine-translation-based pipeline model in zero-shot cross-lingual settings.
Related Work ::: Cross-Lingual NLG
Several previous methods have been proposed for cross-lingual abstractive summarization. BIBREF7 xnh... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 8 | 4,500 | 5,046 |
33_9 | However, the systems only conduct experiments that generate summaries with different languages from the input language, rather than transferring supervision signals across all language pairs. BIBREF10 kumar2019cross introduce a cross-lingual model for question generation, which uses training data annotated in multiple... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 9 | 5,046 | 5,507 |
33_10 |
Related Work ::: Monolingual Pre-Training
Various training objectives are designed to pretrain text encoders used for general-purpose language representations, such as language modeling BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15, auto-encoding BIBREF16, and machine translation BIBREF17. Apart from pre-training... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 10 | 5,507 | 6,011 |
33_11 |
Related Work ::: Cross-Lingual Pre-Training
Cross-lingual pre-training aims at building universal cross-lingual encoders that can encode multilingual sentences to a shared embedding space. BIBREF20 artetxe2018massively use the sequence encoder of the multilingual translation model BIBREF3 to produce cross-lingual se... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 11 | 6,011 | 6,535 |
33_12 | BIBREF4 xnli propose an alignment loss function to encourage parallel sentences to have similar representations. By pre-training BERT BIBREF13 on corpora of multiple languages, it shows a surprising ability to produce cross-lingual representations BIBREF21. More recently, BIBREF5 xlm extend mask language modeling pre-... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 12 | 6,535 | 7,128 |
33_13 |
Methods
Xnlg is a pre-trained sequence-to-sequence model, which is based on Transformer BIBREF22. Both the encoder and the decoder are supposed to support multiple languages. Following BIBREF5, we use language tag embeddings to distinguish the source and target languages. Given a sentence and its corresponding langu... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 13 | 7,128 | 7,747 |
33_14 |
Methods ::: Pre-Training Tasks ::: Monolingual MLM
The masked language modeling (MLM) BIBREF13 task, also known as the Cloze task BIBREF23, aims at predicting the randomly masked words according to their context. The objective pretrains the bidirectional encoder to obtain contextual representations. Following BIBREF... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 14 | 7,747 | 8,132 |
33_15 | For each masked token, we substitute it with a special token M, a random token, or the unchanged token with a probability of 0.8, 0.1, and 0.1, respectively. Let $x$ denote a sentence from the monolingual training corpus, and $M_{x}$ the set of randomly masked positions. The monolingual MLM loss is defined as: MLM(x) ... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 15 | 8,132 | 8,535 |
33_16 | Notice that language tags are fed into the model for all pre-training tasks.
Methods ::: Pre-Training Tasks ::: Denoising Auto-Encoding (DAE)
We use the denoising auto-encoding (DAE) objective BIBREF24 to pretrain the encoder-decoder attention mechanism. Given sentence $x$ from the monolingual corpus, we use three t... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 16 | 8,535 | 9,037 |
33_17 | Third, we substitute tokens with the special padding token P with a probability of $0.1$. The pre-training objective is to recover the original sentence $x$ by conditioning on $\hat{x}$. The DAE loss is computed via: DAE(x) = -p(x|x) = -i = 1|x|p(xi | x, x<i) where $x_{<i}$ represents the tokens of previous time steps... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 17 | 9,037 | 9,380 |
33_18 |
Methods ::: Pre-Training Tasks ::: Cross-Lingual MLM (XMLM)
Similar to monolingual MLM, the masked token prediction task can be extended to cross-lingual settings BIBREF5. To be specific, given a parallel corpus, we concatenate the pair of bilingual sentences $(x,y)$ to a whole sequence, and use it as the input of M... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 18 | 9,380 | 9,858 |
33_19 | Apart from using monolingual context to predict the masked tokens, XMLM encourages the model to utilize the alignment of bilingual sentences, so that the model learns to map cross-lingual texts into a shared vector space. Similar to eq:mlm, the cross-lingual MLM loss is: XMLM(x,y) = -i Mxp( xi | xMx , yMy)
-i Myp( yi... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 19 | 9,858 | 10,269 |
33_20 |
Methods ::: Pre-Training Tasks ::: Cross-Lingual Auto-Encoding (XAE)
If only DAE is used as the pre-training task for the decoder, we found that the model ignores the target language tag while generating just the same language as the input, caused by the spurious correlation issue BIBREF25. In other words, the DAE l... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 20 | 10,269 | 10,780 |
33_21 | To solve the above problem, we use machine translation as the cross-lingual auto-encoding (XAE) task, which decreases mutual information between the target sentences and the source language tag. XAE can be viewed as the multilingual-version DAE task in the sense that both of them recover the sentence by conditioning o... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 21 | 10,780 | 11,271 |
33_22 |
Methods ::: Pre-Training Protocol
As shown in Figure FIGREF6(b), we propose a two-stage pre-training protocol for Xnlg. The first stage pretrains the encoding components, where the model learns to encode multilingual sentences to a shared embedding space. We consider using MLM and XMLM as the pre-training tasks. | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 22 | 11,271 | 11,587 |
33_23 | The objective of the first stage is to minimize: 1= (x,y) p XMLM(x,y) + x m MLM(x) where ${_{\textnormal {p}}}$ indicates the parallel corpus, and ${_{\textnormal {m}}}$ is the monolingual corpus.
Although the pre-trained encoder in the first stage enables the model to encode multilingual sentences. | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 23 | 11,587 | 11,889 |
33_24 | However, it cannot directly be used in cross-lingual NLG because: 1) encoder-decoder attention is not pre-trained; 2) the decoding algorithm is different between masked language modeling and autoregressive decoding, resulting in the mismatch between pre-training and fine-tuning. Therefore, we conduct decoding pre-trai... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 24 | 11,889 | 12,339 |
33_25 | The objective of the second stage is to minimize: 2 = (x,y) pXAE(x,y) + x mDAE(x)
Methods ::: Fine-Tuning on Downstream NLG Tasks
In the fine-tuning procedure, let us assume that we only have English training data for downstream NLG tasks. According to whether the target language is English, the directions of NLG ca... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 25 | 12,339 | 12,796 |
33_26 |
Methods ::: Fine-Tuning on Downstream NLG Tasks ::: Fine-Tuning for Any-to-Others NLG
Ideally, the model can be fine-tuned towards a new task without losing its cross-lingual ability. However, we observe the catastrophic forgetting phenomenon of target language controllability, if we fine-tune all the model paramete... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 26 | 12,796 | 13,392 |
33_27 |
Methods ::: Fine-Tuning on Downstream NLG Tasks ::: Fine-Tuning for Any-to-English NLG
For the Any-to-English NLG transfer, the decoder always generates English. So we can freeze the encoder parameters, and update the decoder parameters to retain the cross-lingual ability. As an alternative way, we can also fine-tun... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 27 | 13,392 | 13,825 |
33_28 |
Experiments
We conduct experiments over two cross-lingual NLG downstream tasks, i.e., cross-lingual question generation, and cross-lingual abstractive summarization. We compare Xnlg with state-of-the-art cross-lingual pre-trained models, and machine-translation-based pipelines.
Experiments ::: Training Details ::: ... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 28 | 13,825 | 14,336 |
33_29 | In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 29 | 14,336 | 14,817 |
33_30 | Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later st... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 30 | 14,817 | 15,258 |
33_31 | It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
Experiments ::: Training Details ::: Fine-Tuning
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 31 | 15,258 | 15,734 |
33_32 | When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
Experiments ::: Ques... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 32 | 15,734 | 16,302 |
33_33 | In the following experiments, we extend the QG task to the cross-lingual setting. By only using English QG training data, our goal is to generate questions in English or Chinese with the given passage-answer pair in English or Chinese.
We use SQuAD 1.1 BIBREF30 as the English QG dataset. It is a popular English quest... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 33 | 16,302 | 16,721 |
33_34 | Following BIBREF31, we regard the original development set as the test set, and sample 5000 examples from the training data of two datasets as the development sets. For Chinese QG, we follow the default data splits of WebQA BIBREF32. We regard the provided annotated evidence sentences as the input passages instead of ... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 34 | 16,721 | 17,243 |
33_35 | During decoding Chinese, we utilize a subset of vocabulary, which is obtained from the passage sentences of the WebQA dataset.
Experiments ::: Question Generation ::: English-English Question Generation
We first conduct experiments on the supervised English-English QG setting. We compare our model to the following b... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 35 | 17,243 | 17,775 |
33_36 |
Xlm BIBREF5 The current state-of-the-art cross-lingual pre-training model. We initialize the Transformer-based sequence-to-sequence model with pre-trained XLM.
We evaluate models with BLEU-4 (BL-4), ROUGE (RG) and METEOR (MTR) metrics. As shown in Table TABREF16, our model outperforms the baselines, which demonstrat... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 36 | 17,775 | 18,164 |
33_37 |
Experiments ::: Question Generation ::: Chinese-Chinese Question Generation
We conduct experiments on the zero-shot Chinese-Chinese QG task to evaluate the cross-lingual transfer ability. In this task, models are trained with English QG data but evaluated with Chinese QG examples. We include the following models as ... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 37 | 18,164 | 18,723 |
33_38 | We use the Transformer as the translator, which is also trained on the MultiUN dataset.
Pipeline (Xlm) with Google Translator Same to Pipeline (Xlm) but using Google Translator to translate the texts.
We evaluate models by both automatic evaluation metrics and human experts. The automatic metrics scores are computed... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 38 | 18,723 | 19,261 |
33_39 | We randomly select 100 passage-answer pairs from the English QG test set, and use the models to generate questions. Then we present these examples to three experts to ask for the above scores. In Table TABREF17 and Table TABREF18, we present the results for the zero-shot Zh-Zh-QG. The results of monolingual supervised... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 39 | 19,261 | 19,775 |
33_40 | In the human evaluation, our model also obtains significant improvements in terms of relatedness and correctness.
Experiments ::: Question Generation ::: English-Chinese Question Generation
In the zero-shot English-Chinese question generation experiments, we use Xlm and Pipeline (Xlm) as our baselines. Pipeline (Xlm... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 40 | 19,775 | 20,312 |
33_41 | Table TABREF19 shows the human evaluation results, where our model surpasses all the baselines especially in terms of relatedness and correctness.
Experiments ::: Question Generation ::: Chinese-English Question Generation
We also conduct experiments for zero-shot Chinese-English question generation, and adopt the s... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 41 | 20,312 | 20,891 |
33_42 |
Experiments ::: Abstractive Summarization
We conduct experiments on cross-lingual abstractive summarization (AS). AS is the task of converting the input sentences into summaries while preserving the key meanings. For evaluation, we use English/French/Chinese Gigaword to extract the first sentence and the headline of... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 42 | 20,891 | 21,377 |
33_43 |
Experiments ::: Abstractive Summarization ::: Zero-Shot Summarization
In the zero-shot setting, we only use English data for training, and directly evaluate the model on other languages. In Table TABREF22 and Table TABREF23, we present the results for French/Chinese AS, which are evaluated by the ROUGE-1, ROUGE-2 an... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 43 | 21,377 | 21,877 |
33_44 | Comparing with French, there is a larger gap between baselines and our model on zero-shot Chinese AS, which indicates that the error propagation issue is more serious on distant language pairs.
Experiments ::: Ablation Studies ::: Effects of Pre-Training
We conduct ablation studies for pre-training objectives, and t... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 44 | 21,877 | 22,489 |
33_45 |
Experiments ::: Ablation Studies ::: Effects of Fine-Tuning Strategies
As shown in Table TABREF41, we use the En-En-QG and Zh-Zh-QG tasks to analyze the effects of using different fine-tuning strategies. It can be observed that fine-tuning encoder parameters, our model obtain an impressive performance for both Engli... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 45 | 22,489 | 23,047 |
33_46 | We find that fine-tuning decoder hurts cross-lingual decoding, and the model learns to only decodes English words. For only fine-tuning decoder, the performance degrades by a large margin for both languages because of the underfitting issue, which indicates the necessity of fine-tuning encoder.
Experiments ::: Ablati... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 46 | 23,047 | 23,600 |
33_47 | Specifically, we first fine-tune Xnlg on the English AS data, and then fine-tune it on the French or Chinese AS data. We compare with the monolingual supervised model that Xnlg is only fine-tuned on the dataset of the target language. As shown in Figure FIGREF49, we can observe that the cross-lingual supervision impro... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 47 | 23,600 | 24,066 |
33_48 |
Experiments ::: Case Studies
As shown in Figure FIGREF42, we present some examples generated by Xnlg and the baselines in four directions (En-En, En-Zh, Zh-En, and Zh-Zh). When decoding on an unseen language, Xlm tends to generate random output, because it is not designed for cross-lingual NLG. In terms of the pipel... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 48 | 24,066 | 24,551 |
33_49 | For example, when the pipeline model performs Zh-Zh-QG, keywords are translated twice, increasing the risk of mistranslation. In the second example, “atomic bomb” is mistranslated to “nuclear bomb”, resulting in its low correctness. On the contrary, by directly transferring English supervision signals to the other gen... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 49 | 24,551 | 25,163 |
33_50 | With the pre-trained model, we achieve zero-shot cross-lingual NLG on several languages by only fine-tuning once. Experimental results show that our model outperforms the machine-translation-based pipeline model on several cross-lingual NLG tasks. For future work, we would like to improve our pre-training method towar... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 50 | 25,163 | 25,723 |
33_51 |
Figure 2: Overview of the pre-training tasks and the pre-training protocol designed for XNLG.
Table 3: Human evaluation results of zero-shot Chinese-Chinese question generation. Rel is short for relatedness, Flu for fluency, and Corr for correctness. “*” indicates the improvements are significant at p < 0.05.
Table... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 51 | 25,723 | 26,252 |
33_52 |
Table 2: Evaluation results of zero-shot ChineseChinese question generation. Same shorthands apply as in Table 1.
Table 4: Human evaluation results of zero-shot English-Chinese question generation. “*” indicates the improvements are significant at p < 0.05. Same shorthands apply as in Table 3.
Table 5: Human evalua... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 52 | 26,252 | 26,731 |
33_53 |
Table 7: Evaluation results of zero-shot French abstractive summarization. Same shorthands apply as in Table 1.
Table 6: Evaluation results of monolingual supervised abstractive summarization. Same shorthands apply as in Table 1.
Table 8: Evaluation results of zero-shot Chinese abstractive summarization. Same short... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 53 | 26,731 | 27,240 |
33_54 |
Table 10: Effects of different fine-tuning strategies. Dec, Enc and ET represent fine-tuning the parameters of the decoder, encoder, and Transformer layers of encoder, respectively. Same shorthands apply as in Table 1.
Figure 3: Examples of generated questions by XNLG and the baselines in four directions (En-En,En-Z... | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 54 | 27,240 | 27,705 |
33_55 |
Figure 4: ROUGE-2 scores for few-shot French/Chinese abstractive summarization with different training data sizes. | https://arxiv.org/abs/1909.10481 | Cross-Lingual Natural Language Generation via Pre-Training | 55 | 27,705 | 27,821 |
34_0 | Hierarchical Neural Story Generation
We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the m... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 0 | 0 | 606 |
34_1 | Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one.
Introduction
Story-telling is on the frontier of current text generation technology: storie... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 1 | 606 | 1,181 |
34_2 |
We tackle the challenges of story-telling with a hierarchical model, which first generates a sentence called the prompt describing the topic for the story, and then conditions on this prompt when generating the story. Conditioning on the prompt or premise makes it easier to generate consistent stories because they pr... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 2 | 1,181 | 1,615 |
34_3 |
We find that standard sequence-to-sequence (seq2seq) models BIBREF1 applied to hierarchical story generation are prone to degenerating into language models that pay little attention to the writing prompt (a problem that has been noted in other domains, such as dialogue response generation BIBREF2 ). This failure is d... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 3 | 1,615 | 2,215 |
34_4 |
To improve the relevance of the generated story to its prompt, we introduce a fusion mechanism BIBREF3 where our model is trained on top of an pre-trained seq2seq model. To improve over the pre-trained model, the second model must focus on the link between the prompt and the story. For the first time, we show that fu... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 4 | 2,215 | 2,808 |
34_5 | We improve efficiency using a convolutional architecture, allowing whole stories to be encoded in parallel. Existing convolutional architectures only encode a bounded amount of context BIBREF4 , so we introduce a novel gated self-attention mechanism that allows the model to condition on its previous outputs at differe... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 5 | 2,808 | 3,417 |
34_6 |
Experiments show that our fusion and self-attention mechanisms improve over existing techniques on both automated and human evaluation measures. Our new dataset and neural architectures allow for models which can creatively generate longer, more consistent and more fluent passages of text. Human judges prefer our hie... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 6 | 3,417 | 4,129 |
34_7 | The prompts have a large diversity of topic, length, and detail. The stories must be at least 30 words, avoid general profanity and inappropriate content, and should be inspired by the prompt (but do not necessarily have to fulfill every requirement). Figure FIGREF1 shows an example.
We scraped three years of prompts... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 7 | 4,129 | 4,769 |
34_8 | We reserve 5% of the prompts for a validation set and 5% for a test set, and present additional statistics about the dataset in Table TABREF4 .
For our experiments, we limit the length of the stories to 1000 words maximum and limit the vocabulary size for the prompts and the stories to words appearing more than 10 ti... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 8 | 4,769 | 5,246 |
34_9 | As the dataset is scraped from an online forum, the number of rare words and misspellings is quite large, so modeling the full vocabulary is challenging and computationally intensive.
Approach
The challenges of WritingPrompts are primarily in modeling long-range dependencies and conditioning on an abstract, high-lev... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 9 | 5,246 | 5,888 |
34_10 | We will evaluate strategies to address these challenges in the following sections.
Hierarchical Story Generation
High-level structure is integral to good stories, but language models generate on a strictly-word-by-word basis and so cannot explicitly make high-level plans. We introduce the ability to plan by decompos... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 10 | 5,888 | 6,493 |
34_11 | Conditioning on the prompt makes it easier for the story to remain consistent and also have structure at a level beyond single phrases.
Efficient Learning with Convolutional Sequence-to-Sequence Model
The length of stories in our dataset is a challenge for RNNs, which process tokens sequentially. To transform prompt... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 11 | 6,493 | 7,095 |
34_12 | In the Conv seq2seq model, the encoder and decoder are connected with attention modules BIBREF7 that perform a weighted sum of encoder outputs, using attention at each layer of the decoder.
Modeling Unbounded Context with Gated Multi-Scale Self-attention
CNNs can only model a bounded context window, preventing the m... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 12 | 7,095 | 7,660 |
34_13 | The self-attention mechanism improves the model's ability to extract long-range context with limited computational impact due to parallelism.
Gated Attention: Similar to BIBREF9 , we use multi-head attention to allow each head to attend to information at different positions. However, the queries, keys and values are ... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 13 | 7,660 | 8,206 |
34_14 |
Multi-Scale Attention: Further, we propose to have each head operating at a different time scale, depicted in Figure FIGREF7 . Thus the input to each head is downsampled a different amount—the first head sees the full input, the second every other input timestep, the third every third input timestep, etc. The differe... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 14 | 8,206 | 8,690 |
34_15 |
The output of a single attention head is given by DISPLAYFORM0
where INLINEFORM0 contains the hidden states up to time INLINEFORM1 at layer INLINEFORM2 , and INLINEFORM3 are gated downsampling networks as shown in Figure FIGREF7 . Unlike BIBREF9 , we allow the model to optionally attend to a 0 vector at each times... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 15 | 8,690 | 9,233 |
34_16 | Additionally, we do not allow the self-attention mechanism to attend to the current timestep, only the past.
Improving Relevance to Input Prompt with Model Fusion
Unlike tasks such as translation, where the semantics of the target are fully specified by the source, the generation of stories from prompts is far more ... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 16 | 9,233 | 9,866 |
34_17 | We train a seq2seq model that has access to the hidden states of a pretrained seq2seq model. Doing so can be seen as a type of boosting or residual learning that allows the second model to focus on what the first model failed to learn—such as conditioning on the prompt. To our knowledge, this paper is the first to sho... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 17 | 9,866 | 10,333 |
34_18 |
The cold fusion mechanism of BIBREF3 pretrains a language model and subsequently trains a seq2seq model with a gating mechanism that learns to leverage the final hidden layer of the language model during seq2seq training. We modify this approach by combining two seq2seq models as follows (see Figure FIGREF13 ): DISPL... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 18 | 10,333 | 10,907 |
34_19 | The gated hidden layers are combined by concatenation and followed by more fully connected layers with GLU activations (see Appendix). We use layer normalization BIBREF10 after each fully connected layer.
Story Generation
Sequence-to-sequence neural networks BIBREF1 have achieved state of the art performance on a va... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 19 | 10,907 | 11,465 |
34_20 |
Previous work on story generation has explored seq2seq RNN architectures BIBREF14 , but has focused largely on using various content to inspire the stories. For instance, BIBREF15 uses photos to inspire short paragraphs trained on romance novels, and BIBREF16 chain a series of independent descriptions together into a... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 20 | 11,465 | 12,122 |
34_21 | They find this technique can generate text of the desired genre, but the movie plots are not interpretable (as the model outputs events, not raw text). However, we are not aware of previous work that has used hierarchical generation from a textual premise to improve the coherence and structure of stories.
Hierarchica... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 21 | 12,122 | 12,745 |
34_22 | BIBREF20 generate a discrete latent variable based on the context, then generates text conditioned upon it.
Fusion Models
Previous work has investigated the integration of language models with seq2seq models. The two models can be leveraged together without architectural modifications: BIBREF21 use language models t... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 22 | 12,745 | 13,295 |
34_23 | BIBREF23 combined a trained language model with a trained seq2seq model to learn a gating function that joins them. BIBREF3 propose training the seq2seq model given the fixed language model then learning a gate to filter the information from the language model.
Baselines
We evaluate a number of baselines:
(1) Langu... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 23 | 13,295 | 13,824 |
34_24 |
(2) seq2seq: using LSTMs and convolutional seq2seq architectures, and Conv seq2seq with decoder self-attention.
(3) Ensemble: an ensemble of two Conv seq2seq with self-attention models.
(4) KNN: we also compare with a KNN model to find the closest prompt in the training set for each prompt in the test set. A TF-IDF... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 24 | 13,824 | 14,247 |
34_25 | The retrieved story from the training set is limited to 150 words to match the length of generated stories.
Fusion Training
To train the fusion model, we first pretrain a Conv seq2seq with self-attention model on the WritingPrompts dataset. This pretrained model is fixed and provided to the second Conv seq2seq with ... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 25 | 14,247 | 14,768 |
34_26 | Similar to BIBREF6 , we train using the Nesterov accelerated gradient method BIBREF26 using gradient clipping BIBREF27 . We perform hyperparameter optimization on each of our models by cross-validating with random search on a validation set. We provide model architectures in the appendix.
Generation
We generate stor... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 26 | 14,768 | 15,427 |
34_27 | We find this sampling strategy substantially more effective than beam search, which tends to produce common phrases and repetitive text from the training set BIBREF28 , BIBREF29 . Sentences produced by beam search tend to be short and generic. Completely random sampling can introduce very unlikely words, which can dam... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 27 | 15,427 | 16,018 |
34_28 | To ease human evaluation, we generate stories of 150 words and do not generate unknown word tokens.
For prompt generation, we use a self-attentive GCNN language model trained with the same prompt-side vocabulary as the sequence-to-sequence story generation models. The language model to generate prompts has a validati... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 28 | 16,018 | 16,645 |
34_29 | Many commonly used metrics, such as BLEU for machine translation or ROUGE for summarization, compute an n-gram overlap between the generated text and the human text—however, in our open-ended generation setting, these are not useful. We do not aim to generate a specific story; we want to generate viable and novel stor... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 29 | 16,645 | 17,165 |
34_30 | Perplexity is commonly used to evaluate the quality of language models, and it reflects how fluently the model can produce the correct next word given the preceding words. We use prompt ranking to assess how strongly a model's output depends on its input. Stories are decoded under 10 different prompts—9 randomly sampl... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 30 | 17,165 | 17,775 |
34_31 |
For human evaluation, we use Amazon Mechanical Turk to conduct a triple pairing task. We use each model to generate stories based on held-out prompts from the test set. Then, groups of three stories are presented to the human judges. The stories and their corresponding prompts are shuffled, and human evaluators are a... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 31 | 17,775 | 18,357 |
34_32 | We use Amazon Mechanical Turk to compare the stories from hierarchical generation from a prompt with generation without a prompt. 400 pairs of stories were evaluated by 5 judges each in a blind test.
Results
We analyze the effect of our modeling improvements on the WritingPrompts dataset.
Generation Quality
Our pr... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 32 | 18,357 | 18,897 |
34_33 | In contrast, the baseline Conv seq2seq model copies 10.2 words on average and the KNN baseline copies all 150 words from a story in the training set.
Figure FIGREF27 shows the values of the fusion gates for an example story, averaged at each timestep. The pretrained seq2seq model acts similarly to a language model pr... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 33 | 18,897 | 19,433 |
34_34 | For example, can't is tokenized to ca n't, and the model occasionally produces the first token but misses the second. A similar error is after one line of dialogue, the model may move to another line of dialogue without generating a newline token. A further obstacle is repetition. The model focuses frequently on what ... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 34 | 19,433 | 19,968 |
34_35 | Language models often struggle to model rare words accurately, as the probability distribution over the next word is dominated by more common words. This tends to produce similar prompts, particularly at the start — we see many prompts that start with the man. In contrast, many of the human prompts are very unique (e.... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 35 | 19,968 | 20,459 |
34_36 |
Use of Attention
We analyze the encoder-decoder attention in the fusion model and find that unlike attention maps in machine translation, where each decoder timestep tends to attend to a different word on the encoder-side, the attention map for each decoder timestep looks similar and focuses mainly on salient words ... | https://arxiv.org/abs/1805.04833 | Hierarchical Neural Story Generation | 36 | 20,459 | 21,125 |
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